Loading [a11y]/accessibility-menu.js
Localized support vector machines using Parzen window for incomplete sets of categories | IEEE Conference Publication | IEEE Xplore

Localized support vector machines using Parzen window for incomplete sets of categories


Abstract:

This paper describes a novel approach to pattern classification that combines Parzen window and support vector machines. Pattern classification is usually performed in un...Show More

Abstract:

This paper describes a novel approach to pattern classification that combines Parzen window and support vector machines. Pattern classification is usually performed in universes where all possible categories are defined. Most of the current supervised learning classification techniques do not account for undefined categories. In a universe that is only partially defined, there may be objects that do not fall into the known set of categories. It would be a mistake to always classify these objects as a known category. We propose a Parzen window-based approach which is capable of classifying an object as not belonging to a known class. In our approach we use a Parzen window to identify local neighbors of a test point and train a localized support vector machine on the identified neighbors. Visual category recognition experiments are performed to compare the results of our approach, localized support vector machines using a k-nearest neighbors approach, and global support vector machines. Our experiments show that our Parzen window approach has superior results when testing with incomplete sets, and comparable results when testing with complete sets.
Date of Conference: 05-07 January 2011
Date Added to IEEE Xplore: 10 February 2011
ISBN Information:

ISSN Information:

Conference Location: Kona, HI, USA

References

References is not available for this document.